Article

ETNeXt: Integrated feature engineering and classification framework for BLDC motor fault detection

2025-10-03

Abstract excerpt

<title>Abstract</title> <p>Motor fault detection is critical for industrial reliability, with acoustic signals serving as a key diagnostic tool. This study employs the ETNeXt framework for robust motor acoustic condition classification using a dataset of 2,021 wav files categorized as "good," "broken," or "heavyload." The methodology includes: (i) applying a 7-level Multilevel Discrete Wavelet Transform (MDWT) wi...

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Literature Corpus work: b3a055ef-051c-5a33-8bd7-e51791cc9ad2
DOI: 10.21203/rs.3.rs-7580431/v1

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